Download Application I - i-Tree

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Tree Inventories
Inventories in i-Tree
 Scope
 Type
Pervasive in i-Tree
Concept important photos1.htm
Scope of inventories
Individual tree
 Day-to-day management
 Goal: accurate data for
every tree
Population of trees
 Long-term planning
 Goal: accurate analysis
of forest
Types of Inventories
Complete Inventory
 Day-to-day field management
 Costly, time-consuming
Partial Inventory
 Complete inventory of some
Sample Inventory
 Randomly-selected trees
inventoried for large-scale
 Cost-efficient
 Good for planning
Types II
Sample inventory benefits
Increase public safety
Facilitate short- and long-term planning
Improve public relations
Justify budgets
Estimate tree benefits
Large gain for small investment
i-Tree promotes the value of sampling
Sampling I
Traditional sampling techniques
valid, but tedious for larger areas
i-Tree v. 1.0 includes applications
to automate the process for two
types of plots:
Linear (street) plots/segments
Spatial (park, any area) plots
Sampling II
Linear plot selectors
 ArcView 3.x (legacy program) OR ArcMap
8.3 or 9.x
 GIS files
 Polygon file delimiting study area boundary
 Road shape file (TIGER/Line data)
Manual selection also possible
TIGER/Line files
Topologically Integrated
Geographic Encoding
and Referencing, or
Format used by the United
States Census Bureau to
describe land attributes
such as roads, buildings,
rivers, and lakes.
Shape files free from
ESRI for use in a GIS
Sampling III
Spatial plot selector
Still testing…
ArcMap 8.3 or 9.x
Study area boundary
 Sub-areas or strata--e.g., land
Digital aerial photos (optional)
Manual methods also possible
Concepts I
Random sample
 Data collection in which
every member of the
population has an equal
chance of being selected
 Can sometimes break
population into subgroups
(stratification) for better
 Mind tricks easily, so need
rigorous method
Concepts II
Variance (= square of SD)
Measure of how much individual
samples vary
The less the individual measurements
vary from the mean (average), the more
reliable the mean
In an urban forest, different traits to
investigate (variables) may have
different variances
 E.g., species distribution (high?) vs.
population size (low)
Source: Dave Nowak
and Jeff Walton,
personal communication
(DRG data)
Concepts III
Sample size
How big?
Sample size depends on
 The relationships to be detected (weak  more)
 The significance level sought (high  more)
 The size of the smallest subgroup (small  more)
 The variance of the variables (high  more)
Can be smaller as these factors change,
especially as variance goes down
Source: Dave Nowak, personal communication
Concepts IV
Standard error (SEM)
The Standard Error (Standard Error of the Mean)
calculates how accurately a sample mean
estimates the population mean.
Formula: SEM = SD/N , where SD = “standard
deviation” of the sample, and N = sample size.
Note that as SD goes down or N goes up, SEM
gets smaller—i.e., estimate becomes better.
Commonly represented by “±” after a number.
Are you done yet?!
Source: blogaloutre
Final sampling thoughts
Sampling is our friend
Both tool and product
in i-Tree
The validity of i-Tree
depends critically on
understanding the
process and capability
of sampling